[Misc][Refactor] Generalize linear_method to be quant_method (#4373)

tests/quantization/test_fp8.py

@@ -20,5 +20,5 @@ def test_load_fp16_model(vllm_runner) -> None:
 
     model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model
     fc1 = model.model.decoder.layers[0].fc1
-    assert isinstance(fc1.linear_method, Fp8LinearMethod)
+    assert isinstance(fc1.quant_method, Fp8LinearMethod)
     assert fc1.weight.dtype == torch.float8_e4m3fn

tests/tensorizer_loader/test_tensorizer.py

@@ -50,10 +50,10 @@ def test_load_with_tensorizer(mock_agent, tensorizer_config):
     mock_agent_instance.deserialize.return_value = MagicMock()
 
     result = load_with_tensorizer(tensorizer_config,
-                                  linear_method=mock_linear_method)
+                                  quant_method=mock_linear_method)
 
     mock_agent.assert_called_once_with(tensorizer_config,
-                                       linear_method=mock_linear_method)
+                                       quant_method=mock_linear_method)
     mock_agent_instance.deserialize.assert_called_once()
     assert result == mock_agent_instance.deserialize.return_value
 

vllm/lora/layers.py

@@ -389,10 +389,9 @@ class ColumnParallelLinearWithLoRA(BaseLayerWithLoRA):
         self.indices = base_indices
         self.indices_len = indices_len
 
-    def apply_weights(self, x: torch.Tensor,
-                      bias: Optional[torch.Tensor]) -> torch.Tensor:
-        output = self.base_layer.linear_method.apply_weights(
-            self.base_layer, x, bias)
+    def apply(self, x: torch.Tensor,
+              bias: Optional[torch.Tensor]) -> torch.Tensor:
+        output = self.base_layer.quant_method.apply(self.base_layer, x, bias)
         _apply_lora(
             x,
             self.lora_a_stacked,
@@ -416,7 +415,7 @@ class ColumnParallelLinearWithLoRA(BaseLayerWithLoRA):
                 if not self.base_layer.skip_bias_add else None)
 
         # Matrix multiply.
-        output_parallel = self.apply_weights(input_, bias)
+        output_parallel = self.apply(input_, bias)
         if self.base_layer.gather_output:
             # All-gather across the partitions.
             output = tensor_model_parallel_all_gather(output_parallel)
@@ -523,10 +522,9 @@ class MergedColumnParallelLinearWithLoRA(ColumnParallelLinearWithLoRA):
                 index, 0, :lora_b[1].shape[1], :lora_b[1].shape[0]].copy_(
                     lora_b[1].T, non_blocking=True)
 
-    def apply_weights(self, x: torch.Tensor,
-                      bias: Optional[torch.Tensor]) -> torch.Tensor:
-        output = self.base_layer.linear_method.apply_weights(
-            self.base_layer, x, bias)
+    def apply(self, x: torch.Tensor,
+              bias: Optional[torch.Tensor]) -> torch.Tensor:
+        output = self.base_layer.quant_method.apply(self.base_layer, x, bias)
         _apply_lora_packed_nslice(
             x,
             self.lora_a_stacked,
@@ -765,10 +763,9 @@ class MergedQKVParallelLinearWithLora(ColumnParallelLinearWithLoRA):
                 index, 0, :lora_a[2].shape[1], :lora_a[2].shape[0]].copy_(
                     lora_a[2].T, non_blocking=True)
 
-    def apply_weights(self, x: torch.Tensor,
-                      bias: Optional[torch.Tensor]) -> torch.Tensor:
-        output = self.base_layer.linear_method.apply_weights(
-            self.base_layer, x, bias)
+    def apply(self, x: torch.Tensor,
+              bias: Optional[torch.Tensor]) -> torch.Tensor:
+        output = self.base_layer.quant_method.apply(self.base_layer, x, bias)
         _apply_lora_packed_nslice(
             x,
             self.lora_a_stacked,
@@ -862,9 +859,8 @@ class RowParallelLinearWithLoRA(BaseLayerWithLoRA):
         self.indices = base_indices
         self.indices_len = indices_len
 
-    def apply_weights(self, x: torch.Tensor) -> torch.Tensor:
-        output = self.base_layer.linear_method.apply_weights(
-            self.base_layer, x)
+    def apply(self, x: torch.Tensor) -> torch.Tensor:
+        output = self.base_layer.quant_method.apply(self.base_layer, x)
         _apply_lora(
             x,
             self.lora_a_stacked,
@@ -897,7 +893,7 @@ class RowParallelLinearWithLoRA(BaseLayerWithLoRA):
             input_parallel = splitted_input[tp_rank].contiguous()
 
         # Matrix multiply.
-        output_parallel = self.apply_weights(input_parallel)
+        output_parallel = self.apply(input_parallel)
         if self.base_layer.reduce_results and self.base_layer.tp_size > 1:
             output_ = tensor_model_parallel_all_reduce(output_parallel)
         else:

vllm/model_executor/layers/linear.py

-from abc import ABC, abstractmethod
+from abc import abstractmethod
 from typing import List, Optional
 
 import torch
 import torch.nn.functional as F
-from torch import nn
 from torch.nn.parameter import Parameter
 
 from vllm.distributed import (divide, get_tensor_model_parallel_rank,
@@ -12,6 +11,8 @@ from vllm.distributed import (divide, get_tensor_model_parallel_rank,
                               tensor_model_parallel_all_gather,
                               tensor_model_parallel_all_reduce)
 from vllm.logger import init_logger
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig, QuantizeMethodBase)
 from vllm.model_executor.utils import set_weight_attrs
 
 logger = init_logger(__name__)
@@ -25,7 +26,7 @@ def adjust_marlin_shard(param, shard_size, shard_offset):
     return shard_size * marlin_tile_size, shard_offset * marlin_tile_size
 
 
-class LinearMethodBase(ABC):
+class LinearMethodBase(QuantizeMethodBase):
     """Base class for different (maybe quantized) linear methods."""
 
     @abstractmethod
@@ -50,22 +51,15 @@ class LinearMethodBase(ABC):
         raise NotImplementedError
 
     @abstractmethod
-    def apply_weights(self,
-                      layer: torch.nn.Module,
-                      x: torch.Tensor,
-                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
         """Apply the weights in layer to the input tensor.
 
         Expects create_weights to have been called before on the layer."""
         raise NotImplementedError
 
-    def process_weights_after_loading(self, layer: nn.Module) -> None:
-        """Process the weight after loading.
-
-        This can be used for example, to transpose weights for computation.
-        """
-        return
-
 
 class UnquantizedLinearMethod(LinearMethodBase):
     """Linear method without quantization.
@@ -92,10 +86,10 @@ class UnquantizedLinearMethod(LinearMethodBase):
         layer.register_parameter("weight", weight)
         set_weight_attrs(weight, extra_weight_attrs)
 
-    def apply_weights(self,
-                      layer: torch.nn.Module,
-                      x: torch.Tensor,
-                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
         weight = layer.weight
         if self.separate_bias_add:
             if bias is not None:
@@ -104,8 +98,8 @@ class UnquantizedLinearMethod(LinearMethodBase):
         return F.linear(x, weight, bias)
 
 
-class ReplicatedLinear(torch.nn.Module):
-    """Replicated linear layer.
+class LinearBase(torch.nn.Module):
+    """Base linear layer.
 
     Args:
         input_size: input dimension of the linear layer.
@@ -113,17 +107,16 @@ class ReplicatedLinear(torch.nn.Module):
         bias: If true, add bias.
         skip_bias_add: If true, skip adding bias but instead return it.
         params_dtype: Data type for the parameters.
-        linear_method: (Maybe quantized) linear method.
+        quant_config: Quantization configure.
     """
 
     def __init__(
         self,
         input_size: int,
         output_size: int,
-        bias: bool = True,
         skip_bias_add: bool = False,
         params_dtype: Optional[torch.dtype] = None,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
 
@@ -134,12 +127,43 @@ class ReplicatedLinear(torch.nn.Module):
         if params_dtype is None:
             params_dtype = torch.get_default_dtype()
         self.params_dtype = params_dtype
-        if linear_method is None:
-            linear_method = UnquantizedLinearMethod()
-        self.linear_method = linear_method
-        self.linear_method.create_weights(self, self.input_size,
-                                          [self.output_size], self.input_size,
-                                          self.output_size, self.params_dtype)
+        if quant_config is None:
+            self.quant_method = UnquantizedLinearMethod()
+        else:
+            self.quant_method = quant_config.get_quant_method(self)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        raise NotImplementedError
+
+
+class ReplicatedLinear(LinearBase):
+    """Replicated linear layer.
+
+    Args:
+        input_size: input dimension of the linear layer.
+        output_size: output dimension of the linear layer.
+        bias: If true, add bias.
+        skip_bias_add: If true, skip adding bias but instead return it.
+        params_dtype: Data type for the parameters.
+        quant_config: Quantization configure.
+    """
+
+    def __init__(
+        self,
+        input_size: int,
+        output_size: int,
+        bias: bool = True,
+        skip_bias_add: bool = False,
+        params_dtype: Optional[torch.dtype] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+    ):
+        super().__init__(input_size, output_size, skip_bias_add, params_dtype,
+                         quant_config)
+
+        self.quant_method.create_weights(self, self.input_size,
+                                         [self.output_size], self.input_size,
+                                         self.output_size, self.params_dtype)
+
         if bias:
             self.bias = Parameter(
                 torch.empty(self.output_size, dtype=self.params_dtype))
@@ -149,12 +173,12 @@ class ReplicatedLinear(torch.nn.Module):
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         bias = self.bias if not self.skip_bias_add else None
-        output = self.linear_method.apply_weights(self, x, bias)
+        output = self.quant_method.apply(self, x, bias)
         output_bias = self.bias if self.skip_bias_add else None
         return output, output_bias
 
 
-class ColumnParallelLinear(torch.nn.Module):
+class ColumnParallelLinear(LinearBase):
     """Linear layer with column parallelism.
 
     The linear layer is defined as Y = XA + b. A is parallelized along
@@ -171,7 +195,7 @@ class ColumnParallelLinear(torch.nn.Module):
                        bias can be fused with other element-wise operations. we
                        skip adding bias but instead return it.
         params_dtype: Data type for the parameters.
-        linear_method: (Maybe quantized) linear method.
+        quant_config: Quantization configure.
         output_sizes: list of output sizes packed into one output, like for QKV
                        the list would be size 3.
     """
@@ -184,34 +208,26 @@ class ColumnParallelLinear(torch.nn.Module):
         gather_output: bool = False,
         skip_bias_add: bool = False,
         params_dtype: Optional[torch.dtype] = None,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
         output_sizes: Optional[List[int]] = None,
     ):
-        super().__init__()
+        super().__init__(input_size, output_size, skip_bias_add, params_dtype,
+                         quant_config)
 
-        # Keep input parameters
-        self.input_size = input_size
-        self.output_size = output_size
         self.gather_output = gather_output
+
         # Divide the weight matrix along the last dimension.
         tp_size = get_tensor_model_parallel_world_size()
         self.output_size_per_partition = divide(output_size, tp_size)
-        self.skip_bias_add = skip_bias_add
-        if params_dtype is None:
-            params_dtype = torch.get_default_dtype()
-        self.params_dtype = params_dtype
-        if linear_method is None:
-            linear_method = UnquantizedLinearMethod()
         if output_sizes is None:
             output_sizes = [output_size]
-        self.linear_method = linear_method
-        self.linear_method.create_weights(self,
-                                          self.input_size,
-                                          [x // tp_size for x in output_sizes],
-                                          self.input_size,
-                                          self.output_size,
-                                          self.params_dtype,
-                                          weight_loader=self.weight_loader)
+        self.quant_method.create_weights(self,
+                                         self.input_size,
+                                         [x // tp_size for x in output_sizes],
+                                         self.input_size,
+                                         self.output_size,
+                                         self.params_dtype,
+                                         weight_loader=self.weight_loader)
         if bias:
             self.bias = Parameter(
                 torch.empty(self.output_size_per_partition,
@@ -239,7 +255,7 @@ class ColumnParallelLinear(torch.nn.Module):
         bias = self.bias if not self.skip_bias_add else None
 
         # Matrix multiply.
-        output_parallel = self.linear_method.apply_weights(self, input_, bias)
+        output_parallel = self.quant_method.apply(self, input_, bias)
         if self.gather_output:
             # All-gather across the partitions.
             output = tensor_model_parallel_all_gather(output_parallel)
@@ -267,7 +283,7 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
                        bias can be fused with other element-wise operations. we
                        skip adding bias but instead return it.
         params_dtype: Data type for the parameters.
-        linear_method: (Maybe quantized) linear method.
+        quant_config: Quantization configure.
     """
 
     def __init__(
@@ -278,13 +294,13 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
         gather_output: bool = False,
         skip_bias_add: bool = False,
         params_dtype: Optional[torch.dtype] = None,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         self.output_sizes = output_sizes
         tp_size = get_tensor_model_parallel_world_size()
         assert all(output_size % tp_size == 0 for output_size in output_sizes)
         super().__init__(input_size, sum(output_sizes), bias, gather_output,
-                         skip_bias_add, params_dtype, linear_method,
+                         skip_bias_add, params_dtype, quant_config,
                          self.output_sizes)
 
     def weight_loader(self,
@@ -384,7 +400,7 @@ class QKVParallelLinear(ColumnParallelLinear):
                        bias can be fused with other element-wise operations. we
                        skip adding bias but instead return it.
         params_dtype: Data type for the parameters.
-        linear_method: (Maybe quantized) linear method.
+        quant_config: Quantization configure.
     """
 
     def __init__(
@@ -396,7 +412,7 @@ class QKVParallelLinear(ColumnParallelLinear):
         bias: bool = True,
         skip_bias_add: bool = False,
         params_dtype: Optional[torch.dtype] = None,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         self.hidden_size = hidden_size
         self.head_size = head_size
@@ -424,7 +440,7 @@ class QKVParallelLinear(ColumnParallelLinear):
         ]
 
         super().__init__(input_size, output_size, bias, False, skip_bias_add,
-                         params_dtype, linear_method, output_sizes)
+                         params_dtype, quant_config, output_sizes)
 
     def weight_loader(self,
                       param: Parameter,
@@ -517,7 +533,7 @@ class QKVParallelLinear(ColumnParallelLinear):
         param_data.copy_(loaded_weight)
 
 
-class RowParallelLinear(torch.nn.Module):
+class RowParallelLinear(LinearBase):
     """Linear layer with row parallelism.
 
     The linear layer is defined as Y = XA + b. A is parallelized along
@@ -540,7 +556,7 @@ class RowParallelLinear(torch.nn.Module):
                        bias can be fused with other element-wise operations.
                        We skip adding bias but instead return it.
         params_dtype: Data type for the parameters.
-        linear_method: (Maybe quantized) linear method.
+        quant_config: Quantization configure.
     """
 
     def __init__(
@@ -552,32 +568,24 @@ class RowParallelLinear(torch.nn.Module):
         skip_bias_add: bool = False,
         params_dtype: Optional[torch.dtype] = None,
         reduce_results: bool = True,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
-        super().__init__()
-        # Keep input parameters
-        self.input_size = input_size
-        self.output_size = output_size
+        super().__init__(input_size, output_size, skip_bias_add, params_dtype,
+                         quant_config)
+
         self.input_is_parallel = input_is_parallel
         self.reduce_results = reduce_results
-        if params_dtype is None:
-            params_dtype = torch.get_default_dtype()
-        self.params_dtype = params_dtype
 
         # Divide the weight matrix along the last dimension.
         self.tp_size = get_tensor_model_parallel_world_size()
         self.input_size_per_partition = divide(input_size, self.tp_size)
-        self.skip_bias_add = skip_bias_add
-        if linear_method is None:
-            linear_method = UnquantizedLinearMethod()
-        self.linear_method = linear_method
-        self.linear_method.create_weights(self,
-                                          self.input_size_per_partition,
-                                          [self.output_size],
-                                          self.input_size,
-                                          self.output_size,
-                                          self.params_dtype,
-                                          weight_loader=self.weight_loader)
+        self.quant_method.create_weights(self,
+                                         self.input_size_per_partition,
+                                         [self.output_size],
+                                         self.input_size,
+                                         self.output_size,
+                                         self.params_dtype,
+                                         weight_loader=self.weight_loader)
 
         if not reduce_results and (bias and not skip_bias_add):
             raise ValueError("When not reduce the results, adding bias to the "
@@ -616,8 +624,7 @@ class RowParallelLinear(torch.nn.Module):
             input_parallel = splitted_input[tp_rank].contiguous()
 
         # Matrix multiply.
-        output_parallel = self.linear_method.apply_weights(
-            self, input_parallel)
+        output_parallel = self.quant_method.apply(self, input_parallel)
         if self.reduce_results and self.tp_size > 1:
             output_ = tensor_model_parallel_all_reduce(output_parallel)
         else:

vllm/model_executor/layers/quantization/__init__.py

@@ -4,7 +4,7 @@ from vllm.model_executor.layers.quantization.aqlm import AQLMConfig
 from vllm.model_executor.layers.quantization.awq import AWQConfig
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
-from vllm.model_executor.layers.quantization.fp8 import FP8Config
+from vllm.model_executor.layers.quantization.fp8 import Fp8Config
 from vllm.model_executor.layers.quantization.gptq import GPTQConfig
 from vllm.model_executor.layers.quantization.marlin import MarlinConfig
 from vllm.model_executor.layers.quantization.squeezellm import SqueezeLLMConfig
@@ -12,7 +12,7 @@ from vllm.model_executor.layers.quantization.squeezellm import SqueezeLLMConfig
 QUANTIZATION_METHODS = {
     "aqlm": AQLMConfig,
     "awq": AWQConfig,
-    "fp8": FP8Config,
+    "fp8": Fp8Config,
     "gptq": GPTQConfig,
     "squeezellm": SqueezeLLMConfig,
     "marlin": MarlinConfig,

vllm/model_executor/layers/quantization/aqlm.py

@@ -9,10 +9,10 @@ import torch.nn.functional as F
 from torch.nn.parameter import Parameter
 
 from vllm import _custom_ops as ops
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               set_weight_attrs)
+from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
+from vllm.model_executor.utils import set_weight_attrs
 
 
 def get_int_dtype(nbits: int) -> torch.dtype:
@@ -207,8 +207,11 @@ class AQLMConfig(QuantizationConfig):
         return cls(in_group_size, nbits_per_codebook, num_code_books,
                    out_group_size)
 
-    def get_linear_method(self) -> "AQLMLinearMethod":
-        return AQLMLinearMethod(self)
+    def get_quant_method(
+            self, layer: torch.nn.Module) -> Optional["AQLMLinearMethod"]:
+        if isinstance(layer, LinearBase):
+            return AQLMLinearMethod(self)
+        return None
 
     def get_scaled_act_names(self) -> List[str]:
         return []
@@ -321,7 +324,7 @@ class AQLMLinearMethod(LinearMethodBase):
         layer.register_parameter("scales", scales)
         set_weight_attrs(scales, extra_weight_attrs)
 
-    def apply_weights(
+    def apply(
         self,
         layer: torch.nn.Module,
         x: torch.Tensor,

vllm/model_executor/layers/quantization/awq.py

@@ -4,10 +4,10 @@ import torch
 from torch.nn.parameter import Parameter
 
 from vllm import _custom_ops as ops
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               set_weight_attrs)
+from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
+from vllm.model_executor.utils import set_weight_attrs
 
 
 class AWQConfig(QuantizationConfig):
@@ -62,8 +62,11 @@ class AWQConfig(QuantizationConfig):
         zero_point = cls.get_from_keys(config, ["zero_point"])
         return cls(weight_bits, group_size, zero_point)
 
-    def get_linear_method(self) -> "AWQLinearMethod":
-        return AWQLinearMethod(self)
+    def get_quant_method(
+            self, layer: torch.nn.Module) -> Optional["AWQLinearMethod"]:
+        if isinstance(layer, LinearBase):
+            return AWQLinearMethod(self)
+        return None
 
     def get_scaled_act_names(self) -> List[str]:
         return ["gelu", "gelu_fast", "gelu_new", "gelu_pytorch_tanh"]
@@ -147,10 +150,10 @@ class AWQLinearMethod(LinearMethodBase):
         layer.register_parameter("scales", scales)
         set_weight_attrs(scales, extra_weight_attrs)
 
-    def apply_weights(self,
-                      layer: torch.nn.Module,
-                      x: torch.Tensor,
-                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
         qweight = layer.qweight
         scales = layer.scales
         qzeros = layer.qzeros

vllm/model_executor/layers/quantization/base_config.py

@@ -2,8 +2,33 @@ from abc import ABC, abstractmethod
 from typing import Any, Dict, List
 
 import torch
+from torch import nn
 
-from vllm.model_executor.layers.linear import LinearMethodBase
+
+class QuantizeMethodBase(ABC):
+    """Base class for different quantized methods."""
+
+    @abstractmethod
+    def create_weights(self, layer: torch.nn.Module, *weight_args,
+                       **extra_weight_attrs):
+        """Create weights for a layer.
+
+        The weights will be set as attributes of the layer."""
+        raise NotImplementedError
+
+    @abstractmethod
+    def apply(self, layer: torch.nn.Module, *args, **kwargs) -> torch.Tensor:
+        """Apply the weights in layer to the input tensor.
+
+        Expects create_weights to have been called before on the layer."""
+        raise NotImplementedError
+
+    def process_weights_after_loading(self, layer: nn.Module) -> None:
+        """Process the weight after loading.
+
+        This can be used for example, to transpose weights for computation.
+        """
+        return
 
 
 class QuantizationConfig(ABC):
@@ -51,8 +76,8 @@ class QuantizationConfig(ABC):
                          "quantization config.")
 
     @abstractmethod
-    def get_linear_method(self) -> LinearMethodBase:
-        """Get the linear method to use for the quantized linear layer."""
+    def get_quant_method(self, layer: torch.nn.Module) -> QuantizeMethodBase:
+        """Get the quantize method to use for the quantized layer."""
         raise NotImplementedError
 
     @abstractmethod

vllm/model_executor/layers/quantization/fp8.py

-from typing import Any, Dict, List, Optional, Tuple
+from typing import Any, Dict, List, Optional
 
 import torch
 from torch.nn import Module
 from torch.nn.parameter import Parameter
 
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               set_weight_attrs)
+from vllm import _custom_ops as ops
+from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
 from vllm.model_executor.layers.quantization.base_config import (
-    QuantizationConfig)
+    QuantizationConfig, QuantizeMethodBase)
+from vllm.model_executor.utils import set_weight_attrs
 
 
-class FP8Config(QuantizationConfig):
+class Fp8Config(QuantizationConfig):
     """Config class for FP8."""
 
     @classmethod
@@ -33,11 +34,14 @@ class FP8Config(QuantizationConfig):
         return []
 
     @classmethod
-    def from_config(cls, config: Dict[str, Any]) -> "FP8Config":
+    def from_config(cls, config: Dict[str, Any]) -> "Fp8Config":
         return cls()
 
-    def get_linear_method(self) -> "Fp8LinearMethod":
-        return Fp8LinearMethod(self)
+    def get_quant_method(
+            self, layer: torch.nn.Module) -> Optional["QuantizeMethodBase"]:
+        if isinstance(layer, LinearBase):
+            return Fp8LinearMethod(self)
+        return None
 
     def get_scaled_act_names(self) -> List[str]:
         return []
@@ -57,7 +61,7 @@ class Fp8LinearMethod(LinearMethodBase):
         quant_config: The quantization config.
     """
 
-    def __init__(self, quant_config: FP8Config):
+    def __init__(self, quant_config: Fp8Config):
         self.quant_config = quant_config
 
     def create_weights(
@@ -86,24 +90,24 @@ class Fp8LinearMethod(LinearMethodBase):
         layer.register_parameter("weight_scaling_factor", w_scale)
 
     def process_weights_after_loading(self, layer: Module) -> None:
-        # Although the linear_method is propagated to all layers,
+        # Although the quant_method is propagated to all layers,
         # only linear layers invoke "create_weights". So we check
         # whether "weight_scaling_facor" is registered to determine
         # whether the layer is a linear layer that requires quantization.
         if not hasattr(layer, "weight_scaling_factor"):
             return
 
-        qweight, weight_scale = per_tensor_quantize(layer.weight)
+        qweight, weight_scale = ops.scaled_fp8_quant(layer.weight)
         # torch._scaled_mm requires column-major in the second
         # input (weight), so we transpose the quantized weight.
         layer.weight = Parameter(qweight.t(), requires_grad=False)
         layer.weight_scaling_factor.data.copy_(weight_scale)
 
-    def apply_weights(self,
-                      layer: torch.nn.Module,
-                      x: torch.Tensor,
-                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
-        qinput, x_scale = per_tensor_quantize(x)
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+        qinput, x_scale = ops.scaled_fp8_quant(x)
         output, _ = torch._scaled_mm(
             qinput,
             layer.weight,
@@ -113,27 +117,3 @@ class Fp8LinearMethod(LinearMethodBase):
             bias=bias,
         )
         return output
-
-
-def per_tensor_quantize(tensor: torch.Tensor) -> Tuple[torch.Tensor, float]:
-    """Quantize a tensor using per-tensor static scaling factor.
-
-    Args:
-        tensor: The input tensor.
-    """
-    finfo = torch.finfo(torch.float8_e4m3fn)
-    # Calculate the scale as dtype max divided by absmax.
-    # Since .abs() creates a new tensor, we use aminmax to get
-    # the min and max first and then calculate the absmax.
-    min_val, max_val = tensor.aminmax()
-    amax = min_val.abs().max(max_val.abs())
-    scale = finfo.max / amax.clamp(min=1e-12)
-    # scale and clamp the tensor to bring it to
-    # the representative range of float8 data type
-    # (as default cast is unsaturated)
-    qweight = (tensor * scale).clamp(min=finfo.min, max=finfo.max)
-    # Return both float8 data and the inverse scale (as float),
-    # as both required as inputs to torch._scaled_mm
-    qweight = qweight.to(torch.float8_e4m3fn)
-    scale = scale.float().reciprocal()
-    return qweight, scale

vllm/model_executor/layers/quantization/gptq.py

@@ -7,10 +7,10 @@ import torch
 from torch.nn.parameter import Parameter
 
 from vllm import _custom_ops as ops
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               set_weight_attrs)
+from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
+from vllm.model_executor.utils import set_weight_attrs
 
 
 class GPTQConfig(QuantizationConfig):
@@ -63,8 +63,11 @@ class GPTQConfig(QuantizationConfig):
         desc_act = cls.get_from_keys(config, ["desc_act"])
         return cls(weight_bits, group_size, desc_act)
 
-    def get_linear_method(self) -> "GPTQLinearMethod":
-        return GPTQLinearMethod(self)
+    def get_quant_method(
+            self, layer: torch.nn.Module) -> Optional["GPTQLinearMethod"]:
+        if isinstance(layer, LinearBase):
+            return GPTQLinearMethod(self)
+        return None
 
     def get_scaled_act_names(self) -> List[str]:
         return []
@@ -194,10 +197,10 @@ class GPTQLinearMethod(LinearMethodBase):
 
         layer.exllama_state = exllama_state
 
-    def apply_weights(self,
-                      layer: torch.nn.Module,
-                      x: torch.Tensor,
-                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
         qweight = layer.qweight
         out_shape = x.shape[:-1] + (qweight.shape[-1], )
         reshaped_x = x.reshape(-1, x.shape[-1])

vllm/model_executor/layers/quantization/marlin.py

@@ -4,10 +4,10 @@ import torch
 from torch.nn.parameter import Parameter
 
 from vllm import _custom_ops as ops
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               set_weight_attrs)
+from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
+from vllm.model_executor.utils import set_weight_attrs
 
 
 class MarlinConfig(QuantizationConfig):
@@ -72,8 +72,11 @@ class MarlinConfig(QuantizationConfig):
         group_size = cls.get_from_keys(config, ["group_size"])
         return cls(group_size)
 
-    def get_linear_method(self) -> "MarlinLinearMethod":
-        return MarlinLinearMethod(self)
+    def get_quant_method(
+            self, layer: torch.nn.Module) -> Optional["MarlinLinearMethod"]:
+        if isinstance(layer, LinearBase):
+            return MarlinLinearMethod(self)
+        return None
 
     def get_scaled_act_names(self) -> List[str]:
         return []
@@ -197,7 +200,7 @@ class MarlinLinearMethod(LinearMethodBase):
         layer.register_parameter("workspace", workspace)
         set_weight_attrs(workspace, extra_weight_attrs)
 
-    def apply_weights(
+    def apply(
         self,
         layer: torch.nn.Module,
         x: torch.Tensor,

vllm/model_executor/layers/quantization/squeezellm.py

@@ -4,10 +4,10 @@ import torch
 from torch.nn.parameter import Parameter
 
 from vllm import _custom_ops as ops
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               set_weight_attrs)
+from vllm.model_executor.layers.linear import LinearBase
 from vllm.model_executor.layers.quantization.base_config import (
-    QuantizationConfig)
+    QuantizationConfig, QuantizeMethodBase)
+from vllm.model_executor.utils import set_weight_attrs
 from vllm.utils import is_hip
 
 
@@ -51,14 +51,18 @@ class SqueezeLLMConfig(QuantizationConfig):
         weight_bits = cls.get_from_keys(config, ["wbits"])
         return cls(weight_bits)
 
-    def get_linear_method(self) -> "SqueezeLLMLinearMethod":
-        return SqueezeLLMLinearMethod(self)
+    def get_quant_method(
+            self,
+            layer: torch.nn.Module) -> Optional["SqueezeLLMLinearMethod"]:
+        if isinstance(layer, LinearBase):
+            return SqueezeLLMLinearMethod(self)
+        return
 
     def get_scaled_act_names(self) -> List[str]:
         return []
 
 
-class SqueezeLLMLinearMethod(LinearMethodBase):
+class SqueezeLLMLinearMethod(QuantizeMethodBase):
     """Linear method for SqueezeLLM.
 
     Args:
@@ -112,10 +116,10 @@ class SqueezeLLMLinearMethod(LinearMethodBase):
         layer.register_parameter("lookup_table", lookup_table)
         set_weight_attrs(lookup_table, extra_weight_attrs)
 
-    def apply_weights(self,
-                      layer: torch.nn.Module,
-                      x: torch.Tensor,
-                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
         qweight = layer.qweight
         lookup_table = layer.lookup_table
         out_shape = x.shape[:-1] + (qweight.shape[-1], )

vllm/model_executor/model_loader/loader.py

@@ -3,8 +3,7 @@ import copy
 import glob
 import os
 from abc import ABC, abstractmethod
-from typing import (TYPE_CHECKING, Any, Dict, Generator, List, Optional, Tuple,
-                    Type)
+from typing import Any, Dict, Generator, List, Optional, Tuple, Type
 
 import torch
 from torch import nn
@@ -13,6 +12,8 @@ from vllm.config import (VLLM_USE_MODELSCOPE, DeviceConfig, LoadConfig,
                          LoadFormat, LoRAConfig, ModelConfig, ParallelConfig,
                          SchedulerConfig, VisionLanguageConfig)
 from vllm.logger import init_logger
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
 from vllm.model_executor.model_loader.tensorizer import (
     TensorizerConfig, is_vllm_serialized_tensorizer, load_with_tensorizer,
     tensorizer_weights_iterator)
@@ -24,9 +25,6 @@ from vllm.model_executor.model_loader.weight_utils import (
     pt_weights_iterator, safetensors_weights_iterator)
 from vllm.model_executor.models.llava import LlavaForConditionalGeneration
 
-if TYPE_CHECKING:
-    from vllm.model_executor.layers.linear import LinearMethodBase
-
 _VISION_MODEL_CLASSES = [
     LlavaForConditionalGeneration,
 ]
@@ -34,11 +32,10 @@ _VISION_MODEL_CLASSES = [
 logger = init_logger(__name__)
 
 
-def _get_linear_method(
+def _get_quantization_config(
         model_config: ModelConfig,
-        load_config: LoadConfig) -> Optional["LinearMethodBase"]:
-    """Get the (maybe quantized) linear method."""
-    linear_method = None
+        load_config: LoadConfig) -> Optional[QuantizationConfig]:
+    """Get the quantization config."""
     if model_config.quantization is not None:
         quant_config = get_quant_config(model_config, load_config)
         capability = torch.cuda.get_device_capability()
@@ -55,9 +52,8 @@ def _get_linear_method(
                 f"{model_config.dtype} is not supported for quantization "
                 f"method {model_config.quantization}. Supported dtypes: "
                 f"{supported_dtypes}")
-
-        linear_method = quant_config.get_linear_method()
-    return linear_method
+        return quant_config
+    return None
 
 
 def _get_model_initialization_kwargs(
@@ -85,10 +81,10 @@ def _initialize_model(
         vision_language_config: Optional[VisionLanguageConfig]) -> nn.Module:
     """Initialize a model with the given configurations."""
     model_class = get_model_architecture(model_config)[0]
-    linear_method = _get_linear_method(model_config, load_config)
+    quant_config = _get_quantization_config(model_config, load_config)
 
     return model_class(config=model_config.hf_config,
-                       linear_method=linear_method,
+                       quant_config=quant_config,
                        **_get_model_initialization_kwargs(
                            model_class, lora_config, vision_language_config))
 
@@ -229,9 +225,11 @@ class DefaultModelLoader(BaseModelLoader):
                                                "fall_back_to_pt_during_load",
                                                True)), )
             for _, module in model.named_modules():
-                linear_method = getattr(module, "linear_method", None)
-                if linear_method is not None:
-                    linear_method.process_weights_after_loading(module)
+                quant_method = getattr(module, "quant_method", None)
+                if quant_method is not None:
+                    quant_method.process_weights_after_loading(module)
+                # FIXME: Remove this after Mixtral is updated
+                # to use quant_method.
                 if hasattr(module, "process_weights_after_loading"):
                     module.process_weights_after_loading()
         return model.eval()
@@ -314,11 +312,11 @@ class TensorizerLoader(BaseModelLoader):
         with set_default_torch_dtype(model_config.dtype):
             with torch.device(device_config.device):
                 model_class = get_model_architecture(model_config)[0]
-                linear_method = _get_linear_method(model_config,
-                                                   self.load_config)
+                quant_config = _get_quantization_config(
+                    model_config, self.load_config)
                 extra_kwargs = _get_model_initialization_kwargs(
                     model_class, lora_config, vision_language_config)
-                extra_kwargs["linear_method"] = linear_method
+                extra_kwargs["quant_config"] = quant_config
 
                 tensorizer_config = copy.copy(self.tensorizer_config)
                 tensorizer_config.model_class = model_class

vllm/model_executor/model_loader/tensorizer.py

@@ -13,7 +13,8 @@ from transformers import PretrainedConfig
 
 from vllm.config import ModelConfig, ParallelConfig
 from vllm.logger import init_logger
-from vllm.model_executor.layers.linear import LinearMethodBase
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     VocabParallelEmbedding)
 
@@ -251,7 +252,7 @@ class TensorizerAgent:
     """
 
     def __init__(self, tensorizer_config: TensorizerConfig,
-                 linear_method: LinearMethodBase, **extra_kwargs):
+                 quant_config: QuantizationConfig, **extra_kwargs):
         if tensorizer_load_fail is not None:
             raise ImportError(
                 "Tensorizer is not installed. Please install tensorizer "
@@ -262,10 +263,10 @@ class TensorizerAgent:
         self.tensorizer_args = (
             self.tensorizer_config._construct_tensorizer_args())
         self.extra_kwargs = extra_kwargs
-        if extra_kwargs.get("linear_method", None) is not None:
-            self.linear_method = extra_kwargs["linear_method"]
+        if extra_kwargs.get("quant_config", None) is not None:
+            self.quant_config = extra_kwargs["quant_config"]
         else:
-            self.linear_method = linear_method
+            self.quant_config = quant_config
         self.model = self._init_model()
 
     def _init_model(self):
@@ -274,7 +275,7 @@ class TensorizerAgent:
         with no_init_or_tensor():
             return self.tensorizer_config.model_class(
                 config=model_args,
-                linear_method=self.linear_method,
+                quant_config=self.quant_config,
                 **self.extra_kwargs)
 
     def _resize_lora_embeddings(self):

vllm/model_executor/models/baichuan.py

@@ -31,11 +31,12 @@ from vllm.distributed import (get_tensor_model_parallel_rank,
                               get_tensor_model_parallel_world_size)
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.layernorm import RMSNorm
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               MergedColumnParallelLinear,
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -77,17 +78,17 @@ class BaiChuanMLP(nn.Module):
         hidden_size: int,
         intermediate_size: int,
         hidden_act: str,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.gate_up_proj = MergedColumnParallelLinear(
             hidden_size, [intermediate_size] * 2,
             bias=False,
-            linear_method=linear_method)
+            quant_config=quant_config)
         self.down_proj = RowParallelLinear(intermediate_size,
                                            hidden_size,
                                            bias=False,
-                                           linear_method=linear_method)
+                                           quant_config=quant_config)
         if hidden_act != "silu":
             raise ValueError(f"Unsupported activation: {hidden_act}. "
                              "Only silu is supported for now.")
@@ -110,7 +111,7 @@ class BaiChuanAttention(nn.Module):
         position_embedding: str,
         rope_theta: float = 10000,
         max_position_embeddings: int = 8192,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.hidden_size = hidden_size
@@ -132,13 +133,13 @@ class BaiChuanAttention(nn.Module):
             self.total_num_heads,
             self.total_num_heads,
             bias=False,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.o_proj = RowParallelLinear(
             self.total_num_heads * self.head_dim,
             hidden_size,
             bias=False,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         # Create the alibi slopes and slice them.
         if self.postion_embedding == "ALIBI":
@@ -184,7 +185,7 @@ class BaiChuanDecoderLayer(nn.Module):
     def __init__(self,
                  config: PretrainedConfig,
                  position_embedding: str,
-                 linear_method: Optional[LinearMethodBase] = None):
+                 quant_config: Optional[QuantizationConfig] = None):
         super().__init__()
         self.hidden_size = config.hidden_size
         rope_theta = getattr(config, "rope_theta", 10000)
@@ -196,13 +197,13 @@ class BaiChuanDecoderLayer(nn.Module):
             position_embedding=position_embedding,
             rope_theta=rope_theta,
             max_position_embeddings=max_position_embeddings,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.mlp = BaiChuanMLP(
             hidden_size=self.hidden_size,
             intermediate_size=config.intermediate_size,
             hidden_act=config.hidden_act,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.input_layernorm = RMSNorm(config.hidden_size,
                                        eps=config.rms_norm_eps)
@@ -243,7 +244,7 @@ class BaiChuanModel(nn.Module):
     def __init__(self,
                  config: PretrainedConfig,
                  position_embedding: str,
-                 linear_method: Optional[LinearMethodBase] = None):
+                 quant_config: Optional[QuantizationConfig] = None):
         super().__init__()
         self.config = config
         self.padding_idx = config.pad_token_id
@@ -254,7 +255,7 @@ class BaiChuanModel(nn.Module):
             config.hidden_size,
         )
         self.layers = nn.ModuleList([
-            BaiChuanDecoderLayer(config, position_embedding, linear_method)
+            BaiChuanDecoderLayer(config, position_embedding, quant_config)
             for _ in range(config.num_hidden_layers)
         ])
         self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@@ -303,13 +304,13 @@ class BaiChuanBaseForCausalLM(nn.Module):
         self,
         config,
         position_embedding: str,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,
     ):
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.model = BaiChuanModel(config, position_embedding, linear_method)
+        self.quant_config = quant_config
+        self.model = BaiChuanModel(config, position_embedding, quant_config)
         self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = Sampler()
@@ -388,13 +389,13 @@ class BaichuanForCausalLM(BaiChuanBaseForCausalLM):
     def __init__(
         self,
         config,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,
     ):
         if config.hidden_size == 4096:  # baichuan2 7b
-            super().__init__(config, "ROPE", linear_method, lora_config)
+            super().__init__(config, "ROPE", quant_config, lora_config)
         else:  # baichuan 13b, baichuan2 13b
-            super().__init__(config, "ALIBI", linear_method, lora_config)
+            super().__init__(config, "ALIBI", quant_config, lora_config)
 
 
 class BaiChuanForCausalLM(BaiChuanBaseForCausalLM):
@@ -403,7 +404,7 @@ class BaiChuanForCausalLM(BaiChuanBaseForCausalLM):
     def __init__(
         self,
         config,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,
     ):
-        super().__init__(config, "ROPE", linear_method, lora_config)
+        super().__init__(config, "ROPE", quant_config, lora_config)

vllm/model_executor/models/bloom.py

@@ -28,10 +28,11 @@ from vllm.distributed import (get_tensor_model_parallel_rank,
                               get_tensor_model_parallel_world_size)
 from vllm.model_executor.layers.activation import get_act_fn
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
-                                               LinearMethodBase,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
 from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     VocabParallelEmbedding)
@@ -70,7 +71,7 @@ class BloomAttention(nn.Module):
     def __init__(
         self,
         config: BloomConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.hidden_size = config.hidden_size
@@ -87,13 +88,13 @@ class BloomAttention(nn.Module):
             self.head_dim,
             self.total_num_heads,
             bias=True,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.dense = RowParallelLinear(
             self.hidden_size,
             self.hidden_size,
             bias=True,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
 
         # Create the alibi slopes and slice them.
@@ -129,21 +130,21 @@ class BloomMLP(nn.Module):
     def __init__(
         self,
         config: BloomConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         hidden_size = config.hidden_size
         self.dense_h_to_4h = ColumnParallelLinear(
             hidden_size,
             4 * hidden_size,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
-        quant_config = getattr(linear_method, "quant_config", None)
+        quant_config = getattr(quant_config, "quant_config", None)
         self.gelu_impl = get_act_fn("gelu", quant_config, 4 * hidden_size)
         self.dense_4h_to_h = RowParallelLinear(
             4 * hidden_size,
             hidden_size,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
@@ -158,17 +159,17 @@ class BloomBlock(nn.Module):
     def __init__(
         self,
         config: BloomConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         hidden_size = config.hidden_size
 
         self.input_layernorm = nn.LayerNorm(hidden_size,
                                             eps=config.layer_norm_epsilon)
-        self.self_attention = BloomAttention(config, linear_method)
+        self.self_attention = BloomAttention(config, quant_config)
         self.post_attention_layernorm = nn.LayerNorm(
             hidden_size, eps=config.layer_norm_epsilon)
-        self.mlp = BloomMLP(config, linear_method)
+        self.mlp = BloomMLP(config, quant_config)
         self.apply_residual_connection_post_layernorm = (
             config.apply_residual_connection_post_layernorm)
 
@@ -214,7 +215,7 @@ class BloomModel(nn.Module):
     def __init__(
         self,
         config: BloomConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.embed_dim = config.hidden_size
@@ -229,7 +230,7 @@ class BloomModel(nn.Module):
 
         # Transformer blocks
         self.h = nn.ModuleList([
-            BloomBlock(config, linear_method)
+            BloomBlock(config, quant_config)
             for _ in range(config.num_hidden_layers)
         ])
 
@@ -262,12 +263,12 @@ class BloomForCausalLM(nn.Module):
     def __init__(
         self,
         config: BloomConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.transformer = BloomModel(config, linear_method)
+        self.quant_config = quant_config
+        self.transformer = BloomModel(config, quant_config)
         self.lm_head_weight = self.transformer.word_embeddings.weight
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = Sampler()

vllm/model_executor/models/chatglm.py

@@ -13,11 +13,12 @@ from vllm.config import LoRAConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.layernorm import RMSNorm
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               MergedColumnParallelLinear,
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -33,7 +34,7 @@ class GLMAttention(nn.Module):
     def __init__(
         self,
         config,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.hidden_size = config.hidden_size
@@ -65,13 +66,13 @@ class GLMAttention(nn.Module):
             self.total_num_heads,
             self.total_num_kv_heads,
             bias=config.add_bias_linear or config.add_qkv_bias,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.dense = RowParallelLinear(
             self.total_num_heads * self.head_dim,
             config.hidden_size,
             bias=config.add_bias_linear,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
 
         # https://huggingface.co/THUDM/chatglm3-6b-32k/blob/e210410255278dd9d74463cf396ba559c0ef801c/modeling_chatglm.py#L141
@@ -123,7 +124,7 @@ class GLMMLP(nn.Module):
     def __init__(
         self,
         config,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
 
@@ -134,7 +135,7 @@ class GLMMLP(nn.Module):
             config.hidden_size,
             [config.ffn_hidden_size] * 2,
             bias=config.add_bias_linear,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
 
         self.activation_func = SiluAndMul()
@@ -144,7 +145,7 @@ class GLMMLP(nn.Module):
             config.ffn_hidden_size,
             config.hidden_size,
             bias=config.add_bias_linear,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
 
     def forward(self, hidden_states):
@@ -166,7 +167,7 @@ class GLMBlock(nn.Module):
     def __init__(
         self,
         config,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.apply_residual_connection_post_layernorm = (
@@ -180,7 +181,7 @@ class GLMBlock(nn.Module):
                                                eps=config.layernorm_epsilon)
 
         # Self attention.
-        self.self_attention = GLMAttention(config, linear_method)
+        self.self_attention = GLMAttention(config, quant_config)
         self.hidden_dropout = config.hidden_dropout
 
         # Layernorm on the attention output
@@ -188,7 +189,7 @@ class GLMBlock(nn.Module):
             config.hidden_size, eps=config.layernorm_epsilon)
 
         # MLP
-        self.mlp = GLMMLP(config, linear_method)
+        self.mlp = GLMMLP(config, quant_config)
 
     def forward(
         self,
@@ -236,7 +237,7 @@ class GLMTransformer(nn.Module):
     def __init__(
         self,
         config,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.post_layer_norm = config.post_layer_norm
@@ -246,7 +247,7 @@ class GLMTransformer(nn.Module):
 
         # Transformer layers.
         self.layers = nn.ModuleList(
-            [GLMBlock(config, linear_method) for i in range(self.num_layers)])
+            [GLMBlock(config, quant_config) for i in range(self.num_layers)])
 
         if self.post_layer_norm:
             layer_norm_func = RMSNorm if config.rmsnorm else LayerNorm
@@ -281,7 +282,7 @@ class ChatGLMModel(nn.Module):
     def __init__(
         self,
         config,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
 
@@ -291,7 +292,7 @@ class ChatGLMModel(nn.Module):
         self.num_layers = config.num_layers
         self.multi_query_group_num = config.multi_query_group_num
         self.kv_channels = config.kv_channels
-        self.encoder = GLMTransformer(config, linear_method)
+        self.encoder = GLMTransformer(config, quant_config)
 
         self.output_layer = ParallelLMHead(config.padded_vocab_size,
                                            config.hidden_size)
@@ -333,13 +334,13 @@ class ChatGLMForCausalLM(nn.Module):
     def __init__(
         self,
         config: ChatGLMConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,
     ):
         super().__init__()
         self.config: ChatGLMConfig = config
-        self.linear_method = linear_method
-        self.transformer = ChatGLMModel(config, linear_method)
+        self.quant_config = quant_config
+        self.transformer = ChatGLMModel(config, quant_config)
         self.lm_head_weight = self.transformer.output_layer.weight
         self.logits_processor = LogitsProcessor(config.padded_vocab_size)
         self.sampler = Sampler()

vllm/model_executor/models/commandr.py

@@ -32,11 +32,12 @@ from vllm.attention import Attention, AttentionMetadata
 from vllm.distributed import (get_tensor_model_parallel_rank,
                               get_tensor_model_parallel_world_size)
 from vllm.model_executor.layers.activation import SiluAndMul
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               MergedColumnParallelLinear,
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -91,7 +92,7 @@ class CohereMLP(nn.Module):
     def __init__(
         self,
         config,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.config = config
@@ -101,13 +102,13 @@ class CohereMLP(nn.Module):
             self.hidden_size,
             [self.intermediate_size] * 2,
             bias=False,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.down_proj = RowParallelLinear(
             self.intermediate_size,
             self.hidden_size,
             bias=False,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.act_fn = SiluAndMul()
 
@@ -123,7 +124,7 @@ class CohereAttention(nn.Module):
     def __init__(
         self,
         config: CohereConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         tp_size = get_tensor_model_parallel_world_size()
@@ -158,13 +159,13 @@ class CohereAttention(nn.Module):
             self.total_num_heads,
             self.total_num_kv_heads,
             bias=False,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.o_proj = RowParallelLinear(
             self.total_num_heads * self.head_dim,
             self.hidden_size,
             bias=False,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.rotary_emb = get_rope(
             self.head_dim,
@@ -218,13 +219,13 @@ class CohereDecoderLayer(nn.Module):
 
     def __init__(self,
                  config: CohereConfig,
-                 linear_method: Optional[LinearMethodBase] = None):
+                 quant_config: Optional[QuantizationConfig] = None):
         super().__init__()
         self.hidden_size = config.hidden_size
 
-        self.self_attn = CohereAttention(config, linear_method=linear_method)
+        self.self_attn = CohereAttention(config, quant_config=quant_config)
 
-        self.mlp = CohereMLP(config, linear_method=linear_method)
+        self.mlp = CohereMLP(config, quant_config=quant_config)
         self.input_layernorm = LayerNorm(param_shape=(config.hidden_size),
                                          eps=config.layer_norm_eps)
 
@@ -257,7 +258,7 @@ class CohereModel(nn.Module):
     def __init__(
         self,
         config: CohereConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.config = config
@@ -265,7 +266,7 @@ class CohereModel(nn.Module):
         self.embed_tokens = VocabParallelEmbedding(config.vocab_size,
                                                    config.hidden_size)
         self.layers = nn.ModuleList([
-            CohereDecoderLayer(config, linear_method=linear_method)
+            CohereDecoderLayer(config, quant_config=quant_config)
             for _ in range(config.num_hidden_layers)
         ])
         self.norm = LayerNorm(param_shape=(config.hidden_size),
@@ -298,14 +299,14 @@ class CohereForCausalLM(nn.Module):
     def __init__(
         self,
         config: CohereConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
+        self.quant_config = quant_config
         self.logits_processor = LogitsProcessor(config.vocab_size,
                                                 scale=config.logit_scale)
-        self.model = CohereModel(config, linear_method)
+        self.model = CohereModel(config, quant_config)
         self.sampler = Sampler()
 
     @torch.no_grad()

vllm/model_executor/models/dbrx.py

@@ -9,11 +9,12 @@ from vllm.distributed import (get_tensor_model_parallel_rank,
                               get_tensor_model_parallel_world_size,
                               tensor_model_parallel_all_reduce)
 from vllm.model_executor.layers.fused_moe import fused_moe
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               QKVParallelLinear,
+from vllm.model_executor.layers.linear import (QKVParallelLinear,
                                                ReplicatedLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -44,7 +45,7 @@ class DbrxRouter(nn.Module):
             self.num_total_experts,
             bias=False,
             params_dtype=params_dtype,
-            linear_method=None,
+            quant_config=None,
         )
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
@@ -63,7 +64,7 @@ class DbrxExperts(nn.Module):
     def __init__(
         self,
         config: DbrxConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
         params_dtype: Optional[torch.dtype] = None,
     ):
         super().__init__()
@@ -165,7 +166,7 @@ class DbrxAttention(nn.Module):
     def __init__(
         self,
         config: DbrxConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.d_model = config.d_model
@@ -183,13 +184,13 @@ class DbrxAttention(nn.Module):
             self.total_num_heads,
             self.total_num_kv_heads,
             bias=False,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.out_proj = RowParallelLinear(
             self.d_model,
             self.d_model,
             bias=False,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.rotary_emb = get_rope(
             self.head_dim,
@@ -244,11 +245,11 @@ class DbrxFusedNormAttention(nn.Module):
     def __init__(
         self,
         config: DbrxConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.d_model = config.d_model
-        self.attn = DbrxAttention(config, linear_method)
+        self.attn = DbrxAttention(config, quant_config)
         self.norm_1 = nn.LayerNorm(self.d_model)
         self.norm_2 = nn.LayerNorm(self.d_model)
 
@@ -278,11 +279,11 @@ class DbrxBlock(nn.Module):
     def __init__(
         self,
         config: DbrxConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
-        self.norm_attn_norm = DbrxFusedNormAttention(config, linear_method)
-        self.ffn = DbrxExperts(config, linear_method)
+        self.norm_attn_norm = DbrxFusedNormAttention(config, quant_config)
+        self.ffn = DbrxExperts(config, quant_config)
 
     def forward(
         self,
@@ -307,7 +308,7 @@ class DbrxModel(nn.Module):
     def __init__(
         self,
         config: DbrxConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.wte = VocabParallelEmbedding(
@@ -315,7 +316,7 @@ class DbrxModel(nn.Module):
             config.d_model,
         )
         self.blocks = nn.ModuleList(
-            [DbrxBlock(config, linear_method) for _ in range(config.n_layers)])
+            [DbrxBlock(config, quant_config) for _ in range(config.n_layers)])
         self.norm_f = nn.LayerNorm(config.d_model, eps=1e-5)
         for module in self.modules():
             if hasattr(module, "bias") and isinstance(module.bias,
@@ -348,13 +349,13 @@ class DbrxForCausalLM(nn.Module):
     def __init__(
         self,
         config: DbrxConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
+        self.quant_config = quant_config
         self.unpadded_vocab_size = config.vocab_size
-        self.transformer = DbrxModel(config, linear_method)
+        self.transformer = DbrxModel(config, quant_config)
         self.lm_head = ParallelLMHead(
             config.vocab_size,
             config.d_model,

vllm/model_executor/models/decilm.py

@@ -29,7 +29,8 @@ import torch
 from transformers import PretrainedConfig
 
 from vllm.config import LoRAConfig
-from vllm.model_executor.layers.linear import LinearMethodBase
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.models.llama import LlamaForCausalLM
 
@@ -55,13 +56,13 @@ class DeciLMForCausalLM(LlamaForCausalLM):
     def __init__(
         self,
         config: Optional[PretrainedConfig] = None,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,
     ) -> None:
         config.num_key_value_heads = max(config.num_key_value_heads_per_layer)
         delattr(config, "num_key_value_heads_per_layer")
         super().__init__(config=config,
-                         linear_method=linear_method,
+                         quant_config=quant_config,
                          lora_config=lora_config)
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):